Automating stock allocation and cost-effective purchasing in automobile leasing
Context & Objectives
An automobile leasing provider in Belgium wanted to optimize its stock and make better purchasing decisions.
The company handles summer/winter tire switches for leasing vehicles, assigning each eligible car to two pairs of tires from the stocks or via new purchase.
In the past, twice a year, the client firstly took several days to draw up a list of possible matches for their tire stock. Analyzing tire allocation data was inefficient in many ways. Employees had little time to analyze the data and to make smarter decisions based on those figures.
Once all the remaining tire stock was allocated, the client had to purchase large quantities of tires for their fleet. Depending on several criteria and constraints, the client was eligible for variable wholesale discounts. So, choosing the right provider and identifying the most cost-effective purchase orders added pressure to another manual, yearly time-consuming process.
To gain time in tire allocation and purchasing decisions, the client wanted an automated process and partnered with Agilytic to:
Drastically reduce the time spent in tire allocation reporting
Reduce costs by optimizing the remaining tire stock
Maximize the potential discounts the leaser can claim to the manufacturers
Approach
Capture constraints for allocation and purchasing
First, we sat down with the client to identify constraints. For tire stock allocation, if one tire of a pair is too worn, the client assigns another pair of tires to the car, according to different rules (e.g., tire specifications & storage locations). For purchasing, there are many tire types and models to consider.
There were additional constraints on the operational side, for example:
Winter and summer tires must be mounted at the same provider
There are minimum order quantities to guarantee for a provider to accept an order
Specific tires sets can be chosen from many similar tires specifications
Optimize allocation with an algorithm
Next, we collected, organized, and cleaned the data. This meant collecting and centralizing all stock history and cleaning the fleet and stock tables according to the defined criteria.
Finally, in just six days of code development, we created an optimization algorithm to automate tire stock allocation by translating the problem and defined criteria into the algorithm.
From there, we set the business's different analysis requirements to filter the data for all possible donor tires and find potential matches for each vehicle.
Development of a purchasing application
With the allocation algorithm developed, the next step was to optimize the purchasing of new tires. To close out the project, we developed a standalone application to recalculate optimal orders every year without the client’s intervention.
Results
With the new optimization algorithm and app, our client was able to:
Allocate all remaining tire stock and save days of analysis time
Immediately save 100.000 € on their next tire order
Go from several days to minutes in preparing the order bill
Make meaningful simulations in a couple of clicks at any time
In less than two minutes, a complete list of allocated tires is returned by the algorithm, respecting all business constraints. Team members who repeated the same tasks month after month can now devote more time to meaningful analysis.
The client also shared that the comprehensive analysis and cost-saving application significantly increased their business users' satisfaction and trust.
